from transformers import TFAutoModelForCausalLM, AutoTokenizer
import tensorflow as tf
import gradio as gr

TITLE = "DialoGPT -- Chatbot"
DESCRIPTION = """<center>This application allows you to talk with a machine.
                 In the back-end is using the DialoGPT model from Microsoft.<br>
                 This model extends GPT2 towards the conversational neural response generetion domain.<br>
                 You can also see the <a href="https://arxiv.org/abs/1911.00536">ArXiv paper</a><br></center>"""
EXAMPLES = [
    ["What is your favorite videogame?"],
    ["What do you do for work?"],
    ["What are your hobbies?"],
    ["What is your favorite food?"],
]
ARTICLE = r"""<center>
              Done by dr. Gabriel Lopez<br>
              For more please visit: <a href='https://sites.google.com/view/dr-gabriel-lopez/home'>My Page</a><br>
              </center>"""

# checkpoint = "ericzhou/DialoGPT-Medium-Rick_v2" #pytorch
# checkpoint = "epeicher/DialoGPT-medium-homer" #pytorch
checkpoint = "microsoft/DialoGPT-medium" #tf
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = TFAutoModelForCausalLM.from_pretrained(checkpoint)

# interaction function
def chat_with_bot(user_input, chat_history_and_input=[]):
    emb_user_input = tokenizer.encode(
        user_input + tokenizer.eos_token, return_tensors="tf"
    )
    if chat_history_and_input == []:
        bot_input_ids = emb_user_input  # first iteration
    else:
        bot_input_ids = tf.concat(
            [chat_history_and_input, emb_user_input], axis=-1
        )  # other iterations
    chat_history_and_input = model.generate(
        bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id
    ).numpy()
    # print
    bot_response = tokenizer.decode(
        chat_history_and_input[:, bot_input_ids.shape[-1] :][0],
        skip_special_tokens=True,
    )
    return bot_response, chat_history_and_input


# gradio interface
in_text = gr.Textbox(value="How was the class?", label="Start chatting!")
out_text = gr.Textbox(value="", label="Chatbot response:")

gr.Interface(
    inputs=[in_text, "state"],
    outputs=[out_text, "state"],
    examples=EXAMPLES,
    title=TITLE,
    description=DESCRIPTION,
    article=ARTICLE,
    fn=chat_with_bot,
    allow_flagging=False,
).launch()